Optimizing power, performance, and area (PPA) at advanced nodes has become an increasingly challenging and complex task. To address these challenges, approaches such as machine learning (ML) and design-technology co-optimization (DTCO) have emerged as promising solutions. However, their effectiveness is limited by the lack of diverse training data and prolonged turnaround times (TAT). Artificial data has been widely used in various fields to address the limitations of real-world data. By augmenting datasets, artificial data improve the robustness of ML models against input perturbations, leading to improved performance. Similarly, in the physical design flow, artificial data has great potential for overcoming the scarcity of real-world circuit data [1], [2], [3]. Artificial circuits proposed in previous studies are typically designed for specific applications. By developing a method to generate artificial circuit which resemble real circuits, we can address data scarcity and TAT challenges in physical design. In this talk, we will discuss how leveraging artificial circuits to explore a wide range of circuit characteristics can enhance ML model performance for unseen real-world circuits and accelerate the PPA exploration flow.